Self-Building Artificial Intelligence and Machine Learning to Empower Big Data Analytics in Smart Cities

Damminda Alahakoon (), Rashmika Nawaratne (), Yan Xu (), Daswin Silva (), Uthayasankar Sivarajah () and Bhumika Gupta ()
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Damminda Alahakoon: La Trobe University
Rashmika Nawaratne: La Trobe University
Yan Xu: Northwestern Polytechnical University
Daswin Silva: La Trobe University
Uthayasankar Sivarajah: University of Bradford
Bhumika Gupta: Institut Mines-Telecom Business School, Research Lab: LITEM

Information Systems Frontiers, 2023, vol. 25, issue 1, No 12, 240 pages

Abstract: Abstract The emerging information revolution makes it necessary to manage vast amounts of unstructured data rapidly. As the world is increasingly populated by IoT devices and sensors that can sense their surroundings and communicate with each other, a digital environment has been created with vast volumes of volatile and diverse data. Traditional AI and machine learning techniques designed for deterministic situations are not suitable for such environments. With a large number of parameters required by each device in this digital environment, it is desirable that the AI is able to be adaptive and self-build (i.e. self-structure, self-configure, self-learn), rather than be structurally and parameter-wise pre-defined. This study explores the benefits of self-building AI and machine learning with unsupervised learning for empowering big data analytics for smart city environments. By using the growing self-organizing map, a new suite of self-building AI is proposed. The self-building AI overcomes the limitations of traditional AI and enables data processing in dynamic smart city environments. With cloud computing platforms, the self-building AI can integrate the data analytics applications that currently work in silos. The new paradigm of the self-building AI and its value are demonstrated using the IoT, video surveillance, and action recognition applications.

Keywords: Big data analytics; Self-building AI; Machine learning; Smart cities; Self-organizing maps (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s10796-020-10056-x

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